WO2017151716A1 - Système de détermination de position tridimensionnelle d'un outil d'essai - Google Patents

Système de détermination de position tridimensionnelle d'un outil d'essai Download PDF

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Publication number
WO2017151716A1
WO2017151716A1 PCT/US2017/020112 US2017020112W WO2017151716A1 WO 2017151716 A1 WO2017151716 A1 WO 2017151716A1 US 2017020112 W US2017020112 W US 2017020112W WO 2017151716 A1 WO2017151716 A1 WO 2017151716A1
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WO
WIPO (PCT)
Prior art keywords
camera
tip
training model
testing tool
light
Prior art date
Application number
PCT/US2017/020112
Other languages
English (en)
Inventor
Scott Cameron ROYSTON
Clark B. Foster
Original Assignee
Truinject Medical Corp.
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Truinject Medical Corp. filed Critical Truinject Medical Corp.
Publication of WO2017151716A1 publication Critical patent/WO2017151716A1/fr

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Classifications

    • GPHYSICS
    • G01MEASURING; TESTING
    • G01BMEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
    • G01B11/00Measuring arrangements characterised by the use of optical techniques
    • G01B11/002Measuring arrangements characterised by the use of optical techniques for measuring two or more coordinates
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00Image analysis
    • G06T7/70Determining position or orientation of objects or cameras
    • G06T7/73Determining position or orientation of objects or cameras using feature-based methods
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00Image analysis
    • G06T7/80Analysis of captured images to determine intrinsic or extrinsic camera parameters, i.e. camera calibration
    • GPHYSICS
    • G09EDUCATION; CRYPTOGRAPHY; DISPLAY; ADVERTISING; SEALS
    • G09BEDUCATIONAL OR DEMONSTRATION APPLIANCES; APPLIANCES FOR TEACHING, OR COMMUNICATING WITH, THE BLIND, DEAF OR MUTE; MODELS; PLANETARIA; GLOBES; MAPS; DIAGRAMS
    • G09B23/00Models for scientific, medical, or mathematical purposes, e.g. full-sized devices for demonstration purposes
    • G09B23/28Models for scientific, medical, or mathematical purposes, e.g. full-sized devices for demonstration purposes for medicine
    • G09B23/285Models for scientific, medical, or mathematical purposes, e.g. full-sized devices for demonstration purposes for medicine for injections, endoscopy, bronchoscopy, sigmoidscopy, insertion of contraceptive devices or enemas
    • GPHYSICS
    • G09EDUCATION; CRYPTOGRAPHY; DISPLAY; ADVERTISING; SEALS
    • G09BEDUCATIONAL OR DEMONSTRATION APPLIANCES; APPLIANCES FOR TEACHING, OR COMMUNICATING WITH, THE BLIND, DEAF OR MUTE; MODELS; PLANETARIA; GLOBES; MAPS; DIAGRAMS
    • G09B23/00Models for scientific, medical, or mathematical purposes, e.g. full-sized devices for demonstration purposes
    • G09B23/28Models for scientific, medical, or mathematical purposes, e.g. full-sized devices for demonstration purposes for medicine
    • G09B23/30Anatomical models
    • GPHYSICS
    • G09EDUCATION; CRYPTOGRAPHY; DISPLAY; ADVERTISING; SEALS
    • G09BEDUCATIONAL OR DEMONSTRATION APPLIANCES; APPLIANCES FOR TEACHING, OR COMMUNICATING WITH, THE BLIND, DEAF OR MUTE; MODELS; PLANETARIA; GLOBES; MAPS; DIAGRAMS
    • G09B9/00Simulators for teaching or training purposes
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04NPICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N23/00Cameras or camera modules comprising electronic image sensors; Control thereof
    • H04N23/90Arrangement of cameras or camera modules, e.g. multiple cameras in TV studios or sports stadiums
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2210/00Indexing scheme for image generation or computer graphics
    • G06T2210/41Medical

Definitions

  • injections may be administered in various locations on the body, such as under the conjunctiva, into arteries, bone marrow, the spine, the sternum, the pleural space of the chest region, the peritoneal cavity, joint spaces, and internal organs. Injections can also be helpful in administering medication directly into anatomic locations that are generating pain. These injections may be administered intravenously (through the vein), intramuscularly (into the muscle), intradermally (beneath the skin), subcutaneously (into the fatty layer of skin), or by way of intraperitoneal injections (into the body cavity). Injections can be performed on humans as well as animals. The methods of administering injections typically vary for different procedures and may depend on the substance being injected, the needle size, or the area of injection.
  • Injections are not limited to treating medical conditions, but may be expanded to treating aesthetic imperfections, restorative cosmetic procedures, procedures for treating migraine, depression, epidurals, orthopedic procedures, self-administered injections, in vitro procedures, or other therapeutic procedures. Many of these procedures are performed through injections of various products into different parts of the body.
  • the aesthetic and therapeutic injection industry comprises two main categories of injectable products: neuromodulators and dermal fillers.
  • the neuromodulator industry commonly utilizes nerve-inhibiting products such as Botox ® , Dysport ® , and Xeomin ® , among others.
  • the dermal filler industry utilizes products administered by providers to patients for orthopedic, cosmetic and therapeutic applications, such as, for example, Juvederm ® , Restylane ® , Belotero ® , Sculptra ® , Artefill ® , Voluma ® , Kybella ® , Durolane ® , and others.
  • the providers or injectors may include plastic surgeons, facial plastic surgeons, oculoplastic surgeons, dermatologists, orthopedist, primar ' care givers, psychologist/psychiatrist, nurse practitioners, dentists, and nurses, among others.
  • the system can further comprise a support structure configured for mounting the first and second cameras.
  • the testing tool can comprise a syringe, a biopsy needle, a catheter, or another type of injection device.
  • the system can further comprise an output device in communication with the processing unit and/or the first and second cameras and configured to generate information regarding injection parameters based on the communications.
  • the first central viewing axis can be at a ninety degree angle with respect to the second central viewing axis.
  • the first camera can be positioned in a superior portion of the anatomic training model and the second camera can be positioned in an inferior portion of the anatomic training model.
  • the first central viewing axis can extend anteriorly and inferiorly.
  • the second central viewing axis can extend anteriorly and superiorly.
  • the one or more resilient layers can comprise at least one elastomeric layer.
  • the training model further can comprise an opaque outer skin layer.
  • the training tool can comprise an optical fiber configured to emit light from the tip of the training tool.
  • bending of light in this disclosure includes its broad ordinary meanings understood by a person of ordinary skill in the art, which include refraction of light.
  • the testing tool 1 10 is in the form of a syringe, but the testing tool 1 10 can include other needle-based devices or catheter devices.
  • the testing tool 110 can include a light source that emits light at the head of the needle, for example, using a fiber optic in the needle.
  • the light source may be one or more LEDs, laser diodes, or any other light emitting device or combination of devices.
  • the camera(s) 120 can send the information detected to a processing unit included in the system.
  • the processing unit may be on the camera(s) 120, the training model 100, the output device 140, or on a separate apparatus.
  • the processing unit can communicate with the output device 140, which can display parameters associated with the injection.
  • the output device 140 can include any type of display useful to a user, such as, for example, a tablet, phone, laptop or desktop computer, television, projector or any other display technology.
  • the tip of the testing tool 1 10 when in use, is inserted into an elastomeric layer 103 of the training model 100 after the tip of the testing tool 110 penetrates the outer layer 102.
  • the elastomeric iayer(s) and the base layer 104 of the training model 100 are optically transmissive, light emitted from the tip of the testing tool 110 may visible from the first camera 120 and/or the second camera 130.
  • the system can determine a 3D location, such as an x-y- z location, of the tip of the testing tool 1 0 when the Sight emitted from the tip of the testing tool 110 is visible from both the first camera 120 and the second camera 130.
  • the location can be output to the output device 140.
  • the location may be provided as an image and/or animation of the tip of the testing tool 110 passing through the training model 100.
  • Figure 4A schematically illustrates a light path 412 from the tip of the testing tool 400 to one of the cameras 420. Other details of the training model including the opaque outer layer, the mounting structure and the second camera are omitted in Figure 4A for clarity of illustration.
  • Figure 4B illustrates a process 450 that the training system can use for making intermediate determinations of the 3D location of the tip of the injection tool 410 of Figure 4A.
  • Figure 5 illustrates a process 500 that may be implemented by the training system to determine a position (e.g., x-y-z position) of the tip of the testing tool 410 using the intermediate determinations obtained by the process 450.
  • the light path 412 when the light path 412 hits an outer surface 408 of the rigid inner layer 404 at a non-zero angle of incidence ⁇ 2, the light path 412 can bend toward an axis 414 normal to the interface 408, that is, the angle of refraction ⁇ 4 being smaller than the angle of incidence ⁇ 2.
  • the refractive mdex of the rigid inner layer 404 is higher than the refractive index of the elastomeric layer 402. If the refractive index of the rigid inner layer 404 is lower than the refractive index of the elastomeric layer 402, the light path 412 can bend away from the axis 414.
  • the light path 412 when the light path 412 hits the inner surface 406 of the rigid inner layer 404, the light path 412 can bend away from an axis 416 normal to the interface 406, that is, the angle of incidence ⁇ 1 being smaller than the angle of refraction ⁇ 3, because air generally has a lower refractive index than a solid medium .
  • the cavity of the training model can have a higher refractive index than the rigid inner layer 404 so that the light path bends toward the axis 416.
  • the processing unit can calculate where the adjusted light path intersects the outer surface of the rigid inner layer.
  • the processing unit can calculate a refraction angle, which is the same as the angle of incidence ⁇ 2 as shown in Figure 4A, using the values of the respective refractive indices.
  • the processing unit can further adjust the light path by the refraction angle ⁇ 2,
  • the processing unit can calculate where the adjusted light path intersects an outer surface of the outer layer(s). In some embodiments, the processing unit can take into account the thicknesses of both the elastomeric layer(s) and the opaque outer skin layer.
  • the processing unit can determine an x-y-z position of the light source, which can be indicative of the 3D location of the tip of the injection tool, by calculating a mid-point between the points PI and P2.
  • the process 500 can then proceed to end block 516.
  • the process 500 can be repeated by restarting at block 502 to track multiple locations of the tip of the testing tool over time.
  • This data can be used to animate the trajectory of the injection on the output device.
  • the animation can be in real-time (which includes at least processing time).
  • the 3D location determination processes described above can advantageously provide accurate 3D location of the tip of the injection tool, thereby providing more helpful feedback in injection training, by taking into account refraction of light as the light enters and leaves the rigid inner layer.
  • the processes can further incorporate different light diffusing and/or transmission properties of different elastomeric layers in order to determine the particular elastomeric layer that the tip of the injection tool has reached.
  • Intrinsic parameters can include linear and nonlinear intrinsic parameters, such as focal length, image sensor format, principal point, lens distortion, and the like.
  • Extrinsic parameters can include position and/or rotation of the camera, and coordinate system transformations from coordinates of a chosen coordinate system to 3D camera coordinates.
  • the intrinsic and/or extrinsic parameters of the cameras in the training system can be refined using empirical data of centroid pixel values and their corresponding 3D location values. For a particular camera or lens type, the adjustment only needs to be performed once.
  • the intrinsic parameters of that particular camera or lens type stay the same after the adjustment.
  • the extrinsic parameters also stay the same as long as the cameras are mounted in the same configuration. This adjustment is more advantageous than having to manually calibrate the cameras out of the head .
  • the emitted light may appear in more than one location because the rigid inner layer and/or elastomeric layer(s) can reflect the light, resulting in a plurality of distinct light spots that can be within in the field of view of a camera.
  • An advantage of a two-camera training system described herein is that a known centroid pixel value from one camera can help determining the centroid value of the second camera in such "noisy" situations due to the reflection of light.
  • Figure 7 illustrates a process 700 for finding the centroid pixel value in noisy situations. The process 700 starts at block 702, The processing unit can then determine a line segment from the first camera with the known centroid pixel value at block 704.
  • the processing unit can proceed to block 714 to determine a subsequent feasible light detection region.
  • the subsequent feasible region can be determined based on the length of the injection tool, which can define the maximum travel distance of the light from where the light was previous seen. If a predetermined number of frames have been viewed, the processing unit can proceed to block 716 to look at the entire viewing field of the second camera, and determine at decision block 718 if light is detected. If light is detected, the processor can proceed to block 710 to determine a u-v position of the centroid on the second camera and then proceed to end block 720. If light is not detected in the entire viewing field, the process 500 can proceed to end block 516,
  • any methods disclosed herein need not be performed in the order recited.
  • the methods disclosed herein include certain actions taken by a practitioner; however, they can also include any third-party instruction of those actions, either expressly or by implication.
  • actions such as "inserting the testing tool” include “instructing insertion of a testing tool.”

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  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Health & Medical Sciences (AREA)
  • Business, Economics & Management (AREA)
  • Educational Administration (AREA)
  • Educational Technology (AREA)
  • General Health & Medical Sciences (AREA)
  • Mathematical Analysis (AREA)
  • Pure & Applied Mathematics (AREA)
  • Chemical & Material Sciences (AREA)
  • Mathematical Physics (AREA)
  • Medical Informatics (AREA)
  • Medicinal Chemistry (AREA)
  • Algebra (AREA)
  • Computational Mathematics (AREA)
  • Mathematical Optimization (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Pulmonology (AREA)
  • Radiology & Medical Imaging (AREA)
  • Multimedia (AREA)
  • Signal Processing (AREA)
  • Instructional Devices (AREA)
  • Length Measuring Devices By Optical Means (AREA)

Abstract

La présente invention concerne un système permettant de déterminer une position tridimensionnelle d'une pointe d'un outil d'essai dans un modèle d'apprentissage anatomique pouvant être utilisé pour un apprentissage d'injection. Un système de pneu peut comprendre des première et seconde caméras positionnées dans le modèle d'apprentissage anatomique. Les première et seconde caméras peuvent chacune détecter une zone de lumière émise à partir de la pointe de l'outil d'essai. Le système peut également comprendre une unité de traitement configurée pour tracer la lumière émise à partir d'un emplacement d'un centroïde de la zone de lumière émise dans la première caméra et un emplacement d'un centroïde de la zone de lumière émise dans la seconde caméra respectivement, et pour calculer une position tridimensionnelle de la pointe distale de l'outil d'essai sur la base des emplacements des centroïdes. Le calcul peut prendre en compte la réfraction de la lumière émise à travers la couche la plus interne du modèle d'apprentissage.
PCT/US2017/020112 2016-03-02 2017-03-01 Système de détermination de position tridimensionnelle d'un outil d'essai WO2017151716A1 (fr)

Applications Claiming Priority (2)

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US201662302328P 2016-03-02 2016-03-02
US62/302,328 2016-03-02

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Cited By (10)

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US9922578B2 (en) 2014-01-17 2018-03-20 Truinject Corp. Injection site training system
US10235904B2 (en) 2014-12-01 2019-03-19 Truinject Corp. Injection training tool emitting omnidirectional light
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